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Multi-Agent Quantitative Analysis System with Azure Cloud Integration

Build a production-ready Multi-Agent Quantitative Analyst system where autonomous AI agents collaborate to scrape market data, analyze trends, and generate professional investment reports. This full-stack application utilizes FastAPI for backend orchestration, Streamlit for the user interface, and integrates Azure PostgreSQL for transaction metadata logging and Azure Blob Storage for report archiving.

13 lectures

What You Will Learn

Mastering the design and implementation of autonomous AI systems using CrewAI
Implementing cloud-native integrations with Azure services (PostgreSQL and Blob Storage)
Building full-stack AI applications with FastAPI backend and Streamlit frontend
Understanding the principles of agent collaboration and task delegation in multi-agent systems
Implementing data persistence strategies for structured and unstructured data in Azure
Applying production best practices, including environment variable management, modular project structure, and data validation
Gaining practical experience in scraping market data, analyzing financial trends, and generating investment reports

System Architecture

Multi-Agent Quantitative Analysis System with Azure Cloud Integration Architecture Diagram

High-level architecture overview of the Multi-Agent Quantitative Analysis System with Azure Cloud Integration .

What You'll Build

  • A Multi-Agent Quantitative Analysis system powered by CrewAI
  • A FastAPI backend for orchestrating agent workflows and managing API endpoints
  • A Streamlit user interface for visualizing investment reports and system performance
  • An Azure PostgreSQL database for logging structured transaction metadata
  • An Azure Blob Storage archive for securely storing unstructured Markdown reports
Multi-Agent Quantitative Analysis System with Azure Cloud Integration
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